NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship 🏆 Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release 🎉
Free Lesson
Intro to Data Science New Release 🎉
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See 🔥
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular 🔥 Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New 🎉 Generative AI for Finance New 🎉 Generative AI for Marketing New 🎉
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular 🔥 Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular 🔥 Data Science R: Machine Learning Designing and Implementing Production MLOps New 🎉 Natural Language Processing for Production (NLP) New 🎉
Find Inspiration
Get Course Recommendation Must Try 💎 An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release 🎉
Free Lessons
Intro to Data Science New Release 🎉
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See 🔥
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > R > UFC Data Scrape using R (UFC Data Analysis Part I)

UFC Data Scrape using R (UFC Data Analysis Part I)

Jian Qiao
Posted on Aug 24, 2017

Introduction:

As an amateeur martial artist, I’m interested in the various styles, like Wing Chun, Krav Maga, Jiu Jitsu, and others... Although it's highly unlikely that one can draw a definite conclusion on which martial art is the most effective one, I am interested to see what findings I can get through this project. The entire project can be found on my Github repository: https://github.com/Jian-Qiao/UFC-Data-Scrapping

The first part is getting the data, which is going to be the main topic of this post. I will explain how I scrape relevant data and process it in detail.

What's UFC:

The Ultimate Fighting Championship (UFC) is a worldwide mixed martial arts competition based in Las Vegas, Nevada. Its first competition was held in November, 1993. Till the day I performed the data scraping, 390 events and 4058 matches has been held around the globe. The purpose of the early Ultimate Fighting Championship competitions was to identify the most effective martial art in a contest with minimal rules between competitors of different fighting disciplines like boxing, Brazilian jiu-jitsu, Sambo, wrestling, Muay Thai, karate, judo, and other styles.

The sport's popularity was also noticed by the sports betting community as BodogLife.com, an online gambling site, stated in July 2007 that in 2007 UFC would surpass boxing for the first time in terms of betting revenues. In fact, the UFC had already broken the pay-per-view industry's all-time records for a single year of business, generating over $222,766,000 in revenue in 2006, surpassing both WWE and boxing.

That makes it a great data set for me to analyze different martial art styles.

Scraping process:

The Website I will be scraping from will be Sherdog.com, a website focusing on mixed martial art competitions. I will use its UFC section for my data scraping, which will be this.

  • R (rvest)
  • SelectorGadget (Chrome add-on)

P.S. SelectorGadget is a very useful tool to pick up a website project; a demo can be found here.

Scraping Events:

As can be seen from the screen-shot I took below, I noticed that the events data is spread into 4 pages.

For each page, there is a table containing all the links to each events. From that table, I was able to get the Date, Name, Location, URL of each event.  I begin by looping through all 4 pages and scraping the events data.

I'm going to append all structured links into an array for later use.

P.S. the reason why I exclude the first 8 data point is because they are the upcoming events rather than the historical ones.

Scraping Matches:

After getting the URL of all pages containing data for each event, the next step will be scraping all matches data from each event page. Inspecting one of the event pages shows that our match data is separated into several areas:

A

Note that the page is structured as 'winner on the left'. So I was able to know the result of each match from the table I scraped. Otherwise, I would have to scrape the 'Win' or 'Loss' tag separately to get the result.

The code is posted below:

The code is separated in 2 parts, corresponding to 2 parts of data. The first part of this code is for getting the gold part on my screen shot, which is structured as a table. The second part of this code is for scraping the red part on my screen shot, which needed more work to pull information from different tags together.

After some cleaning work, I add the Time, Name of the Event.

With both parts set, I was able to bind them together and create a complete data set.

Scraping Fighters:

Fighter data is relatively easy to scrape. Although it's not in one table, each detail is put in a specific tag. With the help of SelectorGadget, I can easily pinpoint each area needed and retrieve it.

However, since UFC has around 24 years of history, some of the data are missing. That will result in a misplace of the following data. A data cleaning process is necessary if I want to use such data in the future. So I did some data cleaning and formatting using regular-expression.

Get Stadium Data:

As I would want to see where each event was held. I will need the geometric data of each stadium. Since I only have the name and the address of each event, I will need another tool to get such data. I used the geocode function to retrieve such latitude and longitude data from Google Map. As Google has a limit on query rate. The code would keep querying the data until it fails, then wait for one hour and keep going.

Also, some of the address was either misspelled or couldn't be found on Google Map. I manually checked Wikipedia.com and manually typed in there.

Result:

I got the data for each event, match, fighter, stadium location and  saved it as a CSV file for later use. I also checked over  the data one last time to check if everything is formatted as I wanted, no typos, no unwanted spaces.

As a result, I have scraped the following:

390 events (Time, Name, Location, URL)

4058 matches ( Event_Name, Match index, Fighter1, Fighter2, Method, Method_Detail, Round, Time, Referee, Fighter1_Url, Fighter2_Url, Event_id, Event Date, Event_Location)

1641 fighters (Name, Birth_Date, Age, Birth_Place, Country, Height, Weight, Association, Class, Fighter_id, Url, Nick Name, Feet (height), Inch (height), PhothUrl)

199 Stadiums (Address, frequency (how many events are held here), Latitude, Longitude)

If you want to have a up-to-date data set in the future, you are very welcome to do so. You can simply download my data, modify my code a little bit, and just update the missing part of my data.

Now, I'm ready to use this data to write a Shiny App and see if there is any interesting findings. Please see UFC Data Analysis - Shiny App ( UFC Data Analysis Part II) 

The skills the author demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

About Author

Jian Qiao

Jian Qiao is a recent graduate of 12-weeks Online Data Science Boot-camp from NYC Data Science Academy. He has earned his M.S. in Quantitative Finance in 2015. Currently working as a data analyst in Almod Diamonds Ltd, he...
View all posts by Jian Qiao >

Related Articles

Capstone
Acquisition Due Dilligence Automation for Smaller Firms
Machine Learning
Beware of Feature Importance for Business Decisions
Meetup
Building a Safer Future
Python
Tech Layoffs: Exploring the Trends and Industry Shifts
Meetup
Analysis of Mass Shootings and Gun Ownership in the United States

Leave a Comment

Cancel reply

You must be logged in to post a comment.

No comments found.

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    © 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application